基于稀疏贝叶斯网络的情绪脑电的有效性脑网络研究  

Research of Effective Network of Emotion Electroencephalogram Based on Sparse Bayesian Network

作　　者：高佳[1] 王蔚[1] 

机构地区：[1]南京师范大学教育科学学院,南京210097

出　　处：《生物医学工程学杂志》2015年第5期945-951,共7页Journal of Biomedical Engineering

基　　金：国家自然科学基金项目(31272511)子项目资助;江苏省中小学综合素质评价指标体系构建项目资助(B-b/2013/01/035);江苏省教育科学"十二五"规划2013年度重点课题资助项目

摘　　要：脑功能网络探索是揭示大脑处理情绪时潜在神经联系的重要手段,稀疏贝叶斯网络(SBN)方法可以分析各区域因果特性及相互影响,逐渐被应用于脑网络的研究中。本文提取了22名被试情绪脑电(EEG)的theta和alpha频段,构建了不同情绪唤醒度的有效性脑网络,并对节点的度、平均聚类系数和特征路径长度进行分析。结果发现:1相比于低唤醒度的EEG信号,左中颞在高唤醒度状态的因果影响都很明显,而右前额的因果影响都不显著;2高唤醒度的平均聚类系数较高,而低唤醒度的特征路径长度较短。Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network （SBN） has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band and alpha band from emotion electroencephalogram （EEG） of 22 subjects, constructed effective networks of different arousal, and analyzed measurements of complex network including degree, average clustering coefficient and characteristic path length. We found that： O compared with EEG signal of low arousal, left middle temporal extensively interacted with other regions in high arousal, while right superior frontal interacted less; ② av- erage clustering coefficient was higher in high arousal and characteristic path length was shorter in low arousal.

关 键 词：稀疏贝叶斯网络 情绪 THETA ALPHA 有效性脑网络 

分 类 号：TN911.7[电子电信—通信与信息系统] R338[电子电信—信息与通信工程]